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An Introduction to Neo4j
An Introduction to Neo4j
An Introduction to Neo4j
An Introduction to Neo4j
An Introduction to Neo4j
Neo4j Product Overview
Neo4j Product Overview
NOSQL, The Web And The Enterprise
An Introduction to Neo4j
An Introduction to Neo4j
private static enum ExampleRelationshipTypes implements
RelationshipType
{
    EXAMPLE
}




GraphDatabaseService graphDb = new EmbeddedGraphDatabase( DB_PATH );
registerShutdownHook( graphDb );




graphDb.shutdown();
Transaction tx = graphDb.beginTx();
try
{
    Node firstNode = graphDb.createNode();
    firstNode.setProperty( NAME_KEY, "Hello" );
    Node secondNode = graphDb.createNode();
    secondNode.setProperty( NAME_KEY, "World" );
 
    firstNode.createRelationshipTo( secondNode,
        ExampleRelationshipTypes.EXAMPLE );
 
    String greeting = firstNode.getProperty( NAME_KEY ) + " "
        + secondNode.getProperty( NAME_KEY );
    System.out.println( greeting );
    tx.success();
}
finally
{
    tx.finish();
}
An Introduction to Neo4j
An Introduction to Neo4j
An Introduction to Neo4j
An Introduction to Neo4j
private void printFriends( Node person )
{
    Traverser traverser = person.traverse(
        Order.BREADTH_FIRST,   //
        StopEvaluator.END_OF_GRAPH, // Graph
        ReturnableEvaluator.ALL_BUT_START_NODE,
        MyRelationshipTypes.KNOWS, // ”KNOWS”
        Direction.OUTGOING ); //
    for ( Node friend : traverser )
    {   //       Node     ”name”
        System.out.println( friend.getProperty( "name" ) );
    }
}
1


            3


1


    2
An Introduction to Neo4j
private static Traverser findHackers( final Node startNode )
{
    TraversalDescription td = Traversal.description()
            .breadthFirst()
            .relationships( RelTypes.CODED_BY, Direction.OUTGOING )
            .relationships( RelTypes.KNOWS, Direction.OUTGOING )
            .evaluator(
                    Evaluators.returnWhereLastRelationshipTypeIs(
RelTypes.CODED_BY ) );
    return td.traverse( startNode );
}

Traverser traverser = findHackers( getNeoNode() );
int numberOfHackers = 0;
for ( Path hackerPath : traverser )
{
    System.out.println( "At depth " + hackerPath.length() + " => "
                        + hackerPath.endNode()
                                .getProperty( "name" ) );
}
Traverser traverser = person.traverse(
    Order.BREADTH_FIRST,
    StopEvaluator.END_OF_GRAPH,
    ReturnableEvaluator.ALL_BUT_START_NODE,
    MyRelationshipTypes.KNOWS,
    Direction.OUTGOING );
for ( Node friend : traverser ){...}
/*
    1. Begin a transaction.
    2. Operate on the graph.
    3. Mark the transaction as successful (or not).
    4. Finish the transaction.
*/
Transaction tx = graphDb.beginTx();
try
{
       ... // any operation that works with the node space
       tx.success();
}
finally
{
       tx.finish();
}
An Introduction to Neo4j
Graph Databases and Endogenous Indices
                                               name property index




views property index                                                                             gender property index




                                                 name=neo4j
                                                 views=56781

             page_rank=0.023                                         cites
                                       cites

                                                                                             name=tenderlove
                                                                                               gender=male
                                                created
                                                                             created
                             created
                                                     date=2007/10

                 cites
                                                                                       follows

                                                           follows
                         created
                                               name=peterneubauer                         follows
         name=graph_blog                                       follows
           views=1000                                                           follows
                                   created




                                                                                             name=ahzf
                                                    name=twarko
                                                      age=30
GraphDatabaseService graphDb = new
EmbeddedGraphDatabase( "path/to/neo4j-db" );
IndexService index = new LuceneIndexService( graphDb );


Node andy = graphDb.createNode();
Node larry = graphDb.createNode();


andy.setProperty( "name", "Andy Wachowski" );
andy.setProperty( "title", "Director" );
larry.setProperty( "name", "Larry Wachowski" );
larry.setProperty( "title", "Director" );
index.index( andy, "name", andy.getProperty( "name" ) );
index.index( andy, "title", andy.getProperty( "title" ) );
index.index( larry, "name", larry.getProperty( "name" ) );
index.index( larry, "title", larry.getProperty( "title" ) );
                                http://wiki.neo4j.org/content/Indexing_with_IndexService
// Return the andy node.
index.getSingleNode( "name", "Andy Wachowski" );


// Containing only the larry node
for ( Node hit : index.getNodes( "name", "Larry Wachowski" ) )
{
    // do something
}


// Containing both andy and larry
for ( Node hit : index.getNodes( "title", "Director" )
{
    // do something
}
                                http://wiki.neo4j.org/content/Indexing_with_IndexService
IndexService index = // your LuceneFulltextIndexService

index.getNodes( "name", "wachowski" ); // --> andy and larry
index.getNodes( "name", "andy" ); // --> andy
index.getNodes( "name", "Andy" ); // --> andy
index.getNodes( "name", "larry Wachowski" ); // --> larry
index.getNodes( "name", "wachowski larry" ); // --> larry
index.getNodes( "name", "wachow* andy" ); // --> andy and larry
index.getNodes( "name", "Andy" ); // --> andy
index.getNodes( "name", "andy" ); // --> andy
index.getNodes( "name", "wachowski" ); // --> andy and larry
index.getNodes( "name", "+wachow* +larry" ); // --> larry
index.getNodes( "name", "andy AND larry" ); // -->
index.getNodes( "name", "andy OR larry" ); // --> andy and larry
index.getNodes( "name", "Wachowski AND larry" ); // --> larry
                                  http://wiki.neo4j.org/content/Indexing_with_IndexService
of a spatial, 2D-index that is explicitly14 Marko A.within a Peter Neubauer
                                          modeled Rodriguez and                         1                                      2



                                                         In order to demonstrate how a quadtree index can be represented an
patial analysis makes use of advanced                indexing structuresis represents a quadtreeset is diagrammed i
                                                      versed, a toy graph data set presented. This data
                                                      ure 8. The top half of Figure 8                   index (vertices 1-9)
 [4, 17]. Quadtrees partition a two-dimensional plane into
 sed upon the spatial density of the points being indexed.                                             type=quad
ow space is partitioned as the density of points increases                                               bl=[0,0]
                                                                                                      tr=[100,100]

 e index.                                                                                                  1
                                                                                            sub                            sub


                                                                 type=quad                                         type=quad                      type=quad
                                                                   bl=[0,0]     2                          3        bl=[50,25]             4       bl=[50,0]
                                                                 tr=[50,100]                                        tr=[75,50]                   tr=[100,100]
                                                                                            type=quad               type=quad
                                                                                               bl=[0,0]              bl=[50,50]
                                                                                             tr=[50,50]            tr=[100,100]
                                                                 type=quad                                                                        type=quad
                                                                   bl=[0,50]    5                 6        9           7                   8        bl=[50,0]
                                                                 tr=[50,100]                                                                      tr=[100,50]
                                                                                                      type=quad
                                                                     [0,100] 1                         bl=[50,25]                                [100,100]
                                                                                                       tr=[62,37]


                                                                                                                            g          f

                                                                                    a       b
                                                                                                                       e
                                                                                5                              7


                                                                                                          9                                         bl=[25,20]
                                                                                                               d           3       h
                                                                                                  c                                                 tr=[90,45]

artition of a plane. This figure is an adaptation of a public
                                                                                                                                           i
d courtesy of David Eppstein.                          6                                                  2 4                                  8 [100,0]
                                                                        [0,0]

 motivations behind this article is to stress the A quadtree index ofof space that contains points of interest. The in
                                            Fig. 8. importance a
An Introduction to Neo4j
An Introduction to Neo4j
TraversalDescription description = Traversal.description()
    .breadthFirst()
    .relationships(Relationships.PAIRED, Direction.OUTGOING)
    .evaluator(Evaluators.excludeStartPosition());


description.traverse( startNode ); // Retrieves the traverser




start programmer=(3) match (programmer)-[:PAIRED]->(pair) return pair




start programmer=(3) match (programmer)-[:PAIRED]->(pair)
where pair.age > 30 return pair, count(*) order by age
skip 5 limit 10
[InComing/Outgoing relationships]
                             # All nodes that A has outgoing relationships to.
                             > start n=node(3) match (n)-->(x) return x
                             ==> Node[4]{name->"Bossman"}
                             ==> Node[5]{name->"Cesar"}
                             > start n=node(3) match (n)<--(x) return x
                             ==> Node[1]{name->David"}


[Match by relationship type]
# All nodes that are Blocked by A.
> start n=node(3) match (n)-[:BLOCKS]->(x) return x
==> Node[5]{name->"Cesar"}

[Multiple relationships]
# The three nodes in the path.
> start a=node(3) match (a)-[:KNOWS]->(b)-[:KNOWS]->(c) return a,b,c
==> a: Node[3]{name->"Anders"}
==> b: Node[4]{name->"Bossman"}
==> c: Node[2]{name->"Emil"}
[Shortest path]
# : find the shortest path between two nodes, as long as the path is max 15
relationships long. Inside of the parenthesis you can write


> start d=node(1), e=node(2) match p = shortestPath( d-[*..15]->e ) return p
==> p: (1)--[KNOWS,2]-->(3)--[KNOWS,0]-->(4)--[KNOWS,3]-->(2)
[Count/ Group Count]
> start n=node(2) match (n)-->(x) return n, count(*)
# The start node and the count of related nodes.
==>
n:         Node[2]{name->"A",property->13}
count(*): 3


> start n=node(2) match (n)-[r]->() return type(r), count(*)
# The relationship types and their group count.
==>
TYPE(r):   KNOWS
count(*): 3
[SUM/AVG/MAX/COLLECT]
> start n=node(2,3,4) return sum(n.property)
==> 90
> start n=node(2,3,4) return avg(n.property)
==> 30.0
> start n=node(2,3,4) return max(n.property)
==> 44
> start n=node(2,3,4) return collect(n.property)
==> List(13, 33, 44)
An Introduction to Neo4j
An Introduction to Neo4j
An Introduction to Neo4j
An Introduction to Neo4j
An Introduction to Neo4j
An Introduction to Neo4j
An Introduction to Neo4j
An Introduction to Neo4j
An Introduction to Neo4j

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An Introduction to Neo4j

  • 8. NOSQL, The Web And The Enterprise
  • 11. private static enum ExampleRelationshipTypes implements RelationshipType {     EXAMPLE } GraphDatabaseService graphDb = new EmbeddedGraphDatabase( DB_PATH ); registerShutdownHook( graphDb ); graphDb.shutdown();
  • 12. Transaction tx = graphDb.beginTx(); try {     Node firstNode = graphDb.createNode();     firstNode.setProperty( NAME_KEY, "Hello" );     Node secondNode = graphDb.createNode();     secondNode.setProperty( NAME_KEY, "World" );       firstNode.createRelationshipTo( secondNode,         ExampleRelationshipTypes.EXAMPLE );       String greeting = firstNode.getProperty( NAME_KEY ) + " "         + secondNode.getProperty( NAME_KEY );     System.out.println( greeting );     tx.success(); } finally {     tx.finish(); }
  • 17. private void printFriends( Node person ) {     Traverser traverser = person.traverse(         Order.BREADTH_FIRST, //         StopEvaluator.END_OF_GRAPH, // Graph         ReturnableEvaluator.ALL_BUT_START_NODE,         MyRelationshipTypes.KNOWS, // ”KNOWS”         Direction.OUTGOING ); //     for ( Node friend : traverser )     { // Node ”name”         System.out.println( friend.getProperty( "name" ) );     } }
  • 18. 1 3 1 2
  • 20. private static Traverser findHackers( final Node startNode ) {     TraversalDescription td = Traversal.description()             .breadthFirst()             .relationships( RelTypes.CODED_BY, Direction.OUTGOING )             .relationships( RelTypes.KNOWS, Direction.OUTGOING )             .evaluator(                     Evaluators.returnWhereLastRelationshipTypeIs( RelTypes.CODED_BY ) );     return td.traverse( startNode ); } Traverser traverser = findHackers( getNeoNode() ); int numberOfHackers = 0; for ( Path hackerPath : traverser ) {     System.out.println( "At depth " + hackerPath.length() + " => "                         + hackerPath.endNode()                                 .getProperty( "name" ) ); }
  • 21. Traverser traverser = person.traverse(     Order.BREADTH_FIRST,     StopEvaluator.END_OF_GRAPH,     ReturnableEvaluator.ALL_BUT_START_NODE,     MyRelationshipTypes.KNOWS,     Direction.OUTGOING ); for ( Node friend : traverser ){...}
  • 22. /* 1. Begin a transaction. 2. Operate on the graph. 3. Mark the transaction as successful (or not). 4. Finish the transaction. */ Transaction tx = graphDb.beginTx(); try { ... // any operation that works with the node space tx.success(); } finally { tx.finish(); }
  • 24. Graph Databases and Endogenous Indices name property index views property index gender property index name=neo4j views=56781 page_rank=0.023 cites cites name=tenderlove gender=male created created created date=2007/10 cites follows follows created name=peterneubauer follows name=graph_blog follows views=1000 follows created name=ahzf name=twarko age=30
  • 25. GraphDatabaseService graphDb = new EmbeddedGraphDatabase( "path/to/neo4j-db" ); IndexService index = new LuceneIndexService( graphDb ); Node andy = graphDb.createNode(); Node larry = graphDb.createNode(); andy.setProperty( "name", "Andy Wachowski" ); andy.setProperty( "title", "Director" ); larry.setProperty( "name", "Larry Wachowski" ); larry.setProperty( "title", "Director" ); index.index( andy, "name", andy.getProperty( "name" ) ); index.index( andy, "title", andy.getProperty( "title" ) ); index.index( larry, "name", larry.getProperty( "name" ) ); index.index( larry, "title", larry.getProperty( "title" ) ); http://wiki.neo4j.org/content/Indexing_with_IndexService
  • 26. // Return the andy node. index.getSingleNode( "name", "Andy Wachowski" ); // Containing only the larry node for ( Node hit : index.getNodes( "name", "Larry Wachowski" ) ) { // do something } // Containing both andy and larry for ( Node hit : index.getNodes( "title", "Director" ) { // do something } http://wiki.neo4j.org/content/Indexing_with_IndexService
  • 27. IndexService index = // your LuceneFulltextIndexService index.getNodes( "name", "wachowski" ); // --> andy and larry index.getNodes( "name", "andy" ); // --> andy index.getNodes( "name", "Andy" ); // --> andy index.getNodes( "name", "larry Wachowski" ); // --> larry index.getNodes( "name", "wachowski larry" ); // --> larry index.getNodes( "name", "wachow* andy" ); // --> andy and larry index.getNodes( "name", "Andy" ); // --> andy index.getNodes( "name", "andy" ); // --> andy index.getNodes( "name", "wachowski" ); // --> andy and larry index.getNodes( "name", "+wachow* +larry" ); // --> larry index.getNodes( "name", "andy AND larry" ); // --> index.getNodes( "name", "andy OR larry" ); // --> andy and larry index.getNodes( "name", "Wachowski AND larry" ); // --> larry http://wiki.neo4j.org/content/Indexing_with_IndexService
  • 28. of a spatial, 2D-index that is explicitly14 Marko A.within a Peter Neubauer modeled Rodriguez and 1 2 In order to demonstrate how a quadtree index can be represented an patial analysis makes use of advanced indexing structuresis represents a quadtreeset is diagrammed i versed, a toy graph data set presented. This data ure 8. The top half of Figure 8 index (vertices 1-9) [4, 17]. Quadtrees partition a two-dimensional plane into sed upon the spatial density of the points being indexed. type=quad ow space is partitioned as the density of points increases bl=[0,0] tr=[100,100] e index. 1 sub sub type=quad type=quad type=quad bl=[0,0] 2 3 bl=[50,25] 4 bl=[50,0] tr=[50,100] tr=[75,50] tr=[100,100] type=quad type=quad bl=[0,0] bl=[50,50] tr=[50,50] tr=[100,100] type=quad type=quad bl=[0,50] 5 6 9 7 8 bl=[50,0] tr=[50,100] tr=[100,50] type=quad [0,100] 1 bl=[50,25] [100,100] tr=[62,37] g f a b e 5 7 9 bl=[25,20] d 3 h c tr=[90,45] artition of a plane. This figure is an adaptation of a public i d courtesy of David Eppstein. 6 2 4 8 [100,0] [0,0] motivations behind this article is to stress the A quadtree index ofof space that contains points of interest. The in Fig. 8. importance a
  • 31. TraversalDescription description = Traversal.description() .breadthFirst() .relationships(Relationships.PAIRED, Direction.OUTGOING) .evaluator(Evaluators.excludeStartPosition()); description.traverse( startNode ); // Retrieves the traverser start programmer=(3) match (programmer)-[:PAIRED]->(pair) return pair start programmer=(3) match (programmer)-[:PAIRED]->(pair) where pair.age > 30 return pair, count(*) order by age skip 5 limit 10
  • 32. [InComing/Outgoing relationships] # All nodes that A has outgoing relationships to. > start n=node(3) match (n)-->(x) return x ==> Node[4]{name->"Bossman"} ==> Node[5]{name->"Cesar"} > start n=node(3) match (n)<--(x) return x ==> Node[1]{name->David"} [Match by relationship type] # All nodes that are Blocked by A. > start n=node(3) match (n)-[:BLOCKS]->(x) return x ==> Node[5]{name->"Cesar"} [Multiple relationships] # The three nodes in the path. > start a=node(3) match (a)-[:KNOWS]->(b)-[:KNOWS]->(c) return a,b,c ==> a: Node[3]{name->"Anders"} ==> b: Node[4]{name->"Bossman"} ==> c: Node[2]{name->"Emil"}
  • 33. [Shortest path] # : find the shortest path between two nodes, as long as the path is max 15 relationships long. Inside of the parenthesis you can write > start d=node(1), e=node(2) match p = shortestPath( d-[*..15]->e ) return p ==> p: (1)--[KNOWS,2]-->(3)--[KNOWS,0]-->(4)--[KNOWS,3]-->(2)
  • 34. [Count/ Group Count] > start n=node(2) match (n)-->(x) return n, count(*) # The start node and the count of related nodes. ==> n: Node[2]{name->"A",property->13} count(*): 3 > start n=node(2) match (n)-[r]->() return type(r), count(*) # The relationship types and their group count. ==> TYPE(r): KNOWS count(*): 3
  • 35. [SUM/AVG/MAX/COLLECT] > start n=node(2,3,4) return sum(n.property) ==> 90 > start n=node(2,3,4) return avg(n.property) ==> 30.0 > start n=node(2,3,4) return max(n.property) ==> 44 > start n=node(2,3,4) return collect(n.property) ==> List(13, 33, 44)